Paper Citation Record · LEDGER

Exploring Temporally-Aware Features for Point Tracking

As of 18 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 3 inbound Pith citation observations for arXiv:2501.12218.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.12218 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:25:53.024734Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:48:34.629118Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T19:13:40.636377Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f1fb5de3-52e3-4e71-b754-9d14821a8089 · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

Exploring Temporally-Aware Features for Point Tracking Deep ViT Features as Dense Visual Descriptors

Reference 1

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Source-reported events for the cited work

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Observation df8d1e3d-9d80-4431-8487-69de72985916 · outbound

This paper cites Can Visual Foundation Models Achieve Long-term Point Tracking?.

Exploring Temporally-Aware Features for Point Tracking Can Visual Foundation Models Achieve Long-term Point Tracking?

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:25:53.335334Z

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Observation e8a57346-6430-48bc-a4a8-d80127b89e0a · outbound

This paper cites Longformer: The Long-Document Transformer.

Exploring Temporally-Aware Features for Point Tracking Longformer: The Long-Document Transformer

Reference 3

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Observation 197b79e3-196b-4a9b-9088-4c5cf64fd8f8 · outbound

This paper cites ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth.

Exploring Temporally-Aware Features for Point Tracking ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 4

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Observation 45a42915-dff5-409c-8e1d-6cbcfb248a4b · outbound

This paper cites Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.

Exploring Temporally-Aware Features for Point Tracking Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T17:25:52.822770Z

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Observation 1ac2d4bd-66a3-4933-85da-5cac81f1dd56 · outbound

This paper cites End-to- end object detection with transformers.

Exploring Temporally-Aware Features for Point Tracking End-to- end object detection with transformers

Reference 6

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Source-reported events for the cited work

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Observation 24d79aca-f5ea-42cc-acd2-ef2e5468337b · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

Exploring Temporally-Aware Features for Point Tracking Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 7

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Observation 680728c0-2c90-4f9e-ab37-a94d25040b32 · outbound

This paper cites Schwing, Alexan- der Kirillov, and Rohit Girdhar.

Exploring Temporally-Aware Features for Point Tracking Schwing, Alexan- der Kirillov, and Rohit Girdhar

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.709671Z

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Observation 5d15b052-97ad-46ab-a148-40db0e8c7598 · outbound

This paper cites Cats: Cost ag- gregation transformers for visual correspondence.

Exploring Temporally-Aware Features for Point Tracking Cats: Cost ag- gregation transformers for visual correspondence

Reference 9

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Source-reported events for the cited work

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Observation 0c2d3b00-9826-4409-bc39-4178bed822c9 · outbound

This paper cites Flowtrack: Revisiting optical flow for long- range dense tracking.

Exploring Temporally-Aware Features for Point Tracking Flowtrack: Revisiting optical flow for long- range dense tracking

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.685946Z

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Observation 137c4ca3-5e71-4bad-bd25-2c760ff7765e · outbound

This paper cites Local All-Pair Correspondence for Point Tracking.

Exploring Temporally-Aware Features for Point Tracking Local All-Pair Correspondence for Point Tracking

Reference 11

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Source-reported events for the cited work

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Observation 010d0979-9816-4216-9f41-da61f5eda72c · outbound

This paper cites Tap-vid: A benchmark for track- ing any point in a video.

Exploring Temporally-Aware Features for Point Tracking Tap-vid: A benchmark for track- ing any point in a video

Reference 12

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Observation 448c1432-c5e9-4a11-8440-eb7d6d607a29 · outbound

This paper cites TAPIR: Tracking any point with per-frame initialization and temporal refinement.

Exploring Temporally-Aware Features for Point Tracking TAPIR: Tracking any point with per-frame initialization and temporal refinement

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.659359Z

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Observation 823de783-e178-4460-acb5-08ab3189a3d6 · outbound

This paper cites BootsTAP: Bootstrapped Training for Tracking-Any-Point.

Exploring Temporally-Aware Features for Point Tracking BootsTAP: Bootstrapped Training for Tracking-Any-Point

Reference 14

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Observation b2caa51e-0893-4ef7-a8c3-cc79e0f110d9 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Exploring Temporally-Aware Features for Point Tracking An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

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Observation ac1af52c-c139-4860-9fbe-f898f30d0355 · outbound

This paper cites Kubric: A scalable dataset generator.

Exploring Temporally-Aware Features for Point Tracking Kubric: A scalable dataset generator

Reference 16

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Observation 0294367b-59ed-4810-8f4f-5f1fa4727830 · outbound

This paper cites Asic: Aligning sparse in-the-wild image collections.

Exploring Temporally-Aware Features for Point Tracking Asic: Aligning sparse in-the-wild image collections

Reference 17

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Source-reported events for the cited work

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Observation f23ca91c-8937-4929-b60f-0cd61a28b9f3 · outbound

This paper cites Unsupervised Semantic Segmentation by Distilling Feature Correspondences.

Exploring Temporally-Aware Features for Point Tracking Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Reference 18

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Source-reported events for the cited work

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Observation 631bc528-34d6-4507-a09e-d6e4564abc46 · outbound

This paper cites Harley, Zhaoyuan Fang, and Katerina Fragkiadaki.

Exploring Temporally-Aware Features for Point Tracking Harley, Zhaoyuan Fang, and Katerina Fragkiadaki

Reference 19

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Observation 859e7698-f62a-4705-83d1-56ef663219a1 · outbound

This paper cites Deep residual learning for image recognition.

Exploring Temporally-Aware Features for Point Tracking Deep residual learning for image recognition

Reference 20

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Observation 6eba6a7b-668b-4d63-a3c5-4724726f396f · outbound

This paper cites Mask r-cnn.

Exploring Temporally-Aware Features for Point Tracking Mask r-cnn

Reference 21

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Observation e1863788-987f-4fb0-8c6f-40e1263929d3 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Exploring Temporally-Aware Features for Point Tracking Masked autoencoders are scalable vision learners

Reference 22

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Source-reported events for the cited work

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Observation d43b9532-54ef-42d9-8fd2-8eda16c95edd · outbound

This paper cites INVE: Interactive Neural Video Editing.

Exploring Temporally-Aware Features for Point Tracking INVE: Interactive Neural Video Editing

Reference 23

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Source-reported events for the cited work

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Observation 40c73c63-2e3c-41eb-9add-2cedd0696aa9 · outbound

This paper cites Robust estimation of a location parameter.

Exploring Temporally-Aware Features for Point Tracking Robust estimation of a location parameter

Reference 24

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Source-reported events for the cited work

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Observation c35c9c23-cdd5-46e6-8c39-bee334d2a08a · outbound

This paper cites CoTracker: It is Better to Track Together.

Exploring Temporally-Aware Features for Point Tracking CoTracker: It is Better to Track Together

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T17:25:52.898026Z

Source-reported events for the cited work

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Observation f4c5ea47-0207-48c9-94b1-575b8d537559 · outbound

This paper cites CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos.

Exploring Temporally-Aware Features for Point Tracking CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos

Reference 26

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Observation 2e2af75a-2598-4448-90b0-324a90626f73 · outbound

This paper cites The Kinetics Human Action Video Dataset.

Exploring Temporally-Aware Features for Point Tracking The Kinetics Human Action Video Dataset

Reference 27

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Source-reported events for the cited work

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Observation 4e649591-898f-46de-bac4-1870f925ac30 · outbound

This paper cites Beyond pick-and-place: Tackling robotic stacking of diverse shapes.

Exploring Temporally-Aware Features for Point Tracking Beyond pick-and-place: Tackling robotic stacking of diverse shapes

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.572624Z

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Observation 6f27fc42-3caf-46cc-b0bd-9339c1581b5d · outbound

This paper cites Sfnet: Learning object-aware semantic correspon- dence.

Exploring Temporally-Aware Features for Point Tracking Sfnet: Learning object-aware semantic correspon- dence

Reference 29

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Observation df4563ea-5878-4d45-a9db-f276d196f18d · outbound

This paper cites Grounding Image Matching in 3D with MASt3R.

Exploring Temporally-Aware Features for Point Tracking Grounding Image Matching in 3D with MASt3R

Reference 30

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Observation 165bdea4-3a66-420e-bbd1-ae19ab574cfa · outbound

This paper cites TAPTR: Tracking Any Point with Transformers as Detection.

Exploring Temporally-Aware Features for Point Tracking TAPTR: Tracking Any Point with Transformers as Detection

Reference 31

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Observation ae94f899-8ada-4f60-82d1-a2e68d86ff60 · outbound

This paper cites Tsm: Temporal shift module for efficient video understanding.

Exploring Temporally-Aware Features for Point Tracking Tsm: Temporal shift module for efficient video understanding

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.554863Z

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Observation 5d0092fb-cf2f-419b-aafe-61b6150b3688 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Exploring Temporally-Aware Features for Point Tracking Swin transformer: Hierarchical vision transformer using shifted windows

Reference 33

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Source-reported events for the cited work

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Observation 7e451b42-a409-47cd-a279-9a1632d159a4 · outbound

This paper cites Decoupled Weight Decay Regularization.

Exploring Temporally-Aware Features for Point Tracking Decoupled Weight Decay Regularization

Reference 34

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Observation dcd656ec-e2b1-496e-b26b-dccf310dab75 · outbound

This paper cites Im- proving semantic correspondence with viewpoint-guided spherical maps.

Exploring Temporally-Aware Features for Point Tracking Im- proving semantic correspondence with viewpoint-guided spherical maps

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.537189Z

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Observation 7de4d34d-621a-495c-b7f1-f7254fa85425 · outbound

This paper cites Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization.

Exploring Temporally-Aware Features for Point Tracking Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization

Reference 36

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Observation d70a0047-d9ac-4406-92ce-31635c79012a · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Exploring Temporally-Aware Features for Point Tracking DINOv2: Learning Robust Visual Features without Supervision

Reference 37

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This paper cites Pytorch: An im- perative style, high-performance deep learning library.

Exploring Temporally-Aware Features for Point Tracking Pytorch: An im- perative style, high-performance deep learning library

Reference 38

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Observation 88c7dcac-8c91-4799-87e2-ad248c70f2f9 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

Exploring Temporally-Aware Features for Point Tracking The 2017 DAVIS Challenge on Video Object Segmentation

Reference 39

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Observation 0add2ff8-01f9-4da0-88b4-044fefa7fb7e · outbound

This paper cites Do vision trans- formers see like convolutional neural networks? Advances in neural information processing systems, 34:12116–12128,.

Exploring Temporally-Aware Features for Point Tracking Do vision trans- formers see like convolutional neural networks? Advances in neural information processing systems, 34:12116–12128,

Reference 40

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Observation 4910f3f6-19b2-428e-a5d3-83e4120bb61c · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Exploring Temporally-Aware Features for Point Tracking High-resolution image synthesis with latent diffusion models

Reference 41

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Observation 2149e7cf-18f1-401a-8e26-a26a139b5f38 · outbound

This paper cites Efficient content-based sparse attention with rout- ing transformers.

Exploring Temporally-Aware Features for Point Tracking Efficient content-based sparse attention with rout- ing transformers

Reference 42

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Observation e846d3cc-d34d-44a9-92d3-9290dcef7a57 · outbound

This paper cites Time does tell: Self-supervised time- tuning of dense image representations.

Exploring Temporally-Aware Features for Point Tracking Time does tell: Self-supervised time- tuning of dense image representations

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.473687Z

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Observation bb7c3f9e-5226-4953-8d9b-d67b64fe3f22 · outbound

This paper cites Learning universal semantic correspondences with no supervision and automatic data curation.

Exploring Temporally-Aware Features for Point Tracking Learning universal semantic correspondences with no supervision and automatic data curation

Reference 44

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Observation a4b7fc5e-0d87-4b60-b181-672064ecfb5b · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

Exploring Temporally-Aware Features for Point Tracking Raft: Recurrent all-pairs field transforms for optical flow

Reference 45

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Observation d7add5f6-ab0d-412c-b643-350e180864b1 · outbound

This paper cites Dino-tracker: Taming dino for self-supervised point track- ing in a single video.

Exploring Temporally-Aware Features for Point Tracking Dino-tracker: Taming dino for self-supervised point track- ing in a single video

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.442834Z

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Observation 3fa14fcf-8aec-4fa9-9044-1c42ea5403f9 · outbound

This paper cites Attention is all you need.

Exploring Temporally-Aware Features for Point Tracking Attention is all you need

Reference 47

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Source-reported events for the cited work

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Observation b265589e-eb93-4577-be2d-191bfae5b007 · outbound

This paper cites RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation.

Exploring Temporally-Aware Features for Point Tracking RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation

Reference 48

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Source-reported events for the cited work

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Observation d8a30e79-3eee-4a81-84f5-6b46e51525e0 · outbound

This paper cites Tracking everything everywhere all at once.

Exploring Temporally-Aware Features for Point Tracking Tracking everything everywhere all at once

Reference 49

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Observation 412c5fd4-c2fb-4610-a04b-66f1e99b2885 · outbound

This paper cites Shape of motion: 4d reconstruc- tion from a single video.

Exploring Temporally-Aware Features for Point Tracking Shape of motion: 4d reconstruc- tion from a single video

Reference 50

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Source-reported events for the cited work

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Observation 2d886186-66a3-4fd3-a00a-1e886287baf4 · outbound

This paper cites Rethinking self-supervised correspondence learning: A video frame-level similarity per- spective.

Exploring Temporally-Aware Features for Point Tracking Rethinking self-supervised correspondence learning: A video frame-level similarity per- spective

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.410468Z

Source-reported events for the cited work

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Observation 3020ac08-d997-4f00-979e-a6157cf5d19b · outbound

This paper cites Deconvolutional networks.

Exploring Temporally-Aware Features for Point Tracking Deconvolutional networks

Reference 52

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Source-reported events for the cited work

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Observation c951a5f1-1a11-4b84-8731-186b5e4eac9c · outbound

This paper cites Scaling vision transformers.

Exploring Temporally-Aware Features for Point Tracking Scaling vision transformers

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.374341Z

Source-reported events for the cited work

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Observation 9f2673e3-966d-4c67-a19a-2a1ff206cafe · outbound

This paper cites Pointodyssey: A large-scale synthetic dataset for long-term point tracking.

Exploring Temporally-Aware Features for Point Tracking Pointodyssey: A large-scale synthetic dataset for long-term point tracking

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:25:53.361471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 1e3f09cb-4f43-41a2-900e-513e9de4165d · inbound

Seurat: From Moving Points to Depth cites this paper.

Seurat: From Moving Points to Depth Exploring Temporally-Aware Features for Point Tracking

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:48:34.629118Z

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Observation 81b736a7-1451-4379-9744-d565a8ab7b78 · inbound

Emergent Temporal Correspondences from Video Diffusion Transformers cites this paper.

Emergent Temporal Correspondences from Video Diffusion Transformers Exploring Temporally-Aware Features for Point Tracking

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:13:40.678647Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b4590f58-eee7-4510-981c-5546c821b0ba · inbound

Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders cites this paper.

Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders Exploring Temporally-Aware Features for Point Tracking

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T18:58:10.935188Z

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Unavailable: canonical work link unavailable.